Numerical serial data emerging from large volumes of structured data like weekly sales figures, daily stock-prices, monthly or weekly market-share, rise or fall in customer-satisfaction indices, etc., is known to influence enterprise decision making and strategy creation. Such numerical serial data is typically presented in the form of time-series, where the X axis stands for time and the Y axis stands for the data, such as stock price, volume of transactions, degree of change, or other values. Taking an example of a stock price, such a chart can depict the changes in the stock price as they occur over time. Such numerical serial data presented in the form of time-series is commonly referred to as time-series data.
Successful enterprise decision making, however, largely depends on decision makers' capability to assess the environment around which is likely to influence business in a major way. The signals to be caught from the environment may be related to world politics, global or regional economic policies, competition landscape, socio-political changes in different parts of the world, actions by major stake-holders, and so on. Most of these signals can be usually obtained from unstructured data like News, blogs, market reports and social media, which contains wealth of information that can contribute significantly towards interpretation of structured data, when fused with structured data in a meaningful way.